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Texture Segmentation of Non-cooperative Spacecrafts Images Based on Wavelet and Fractal Dimension

机译:基于小波和分形维数的非合作航天器图像纹理分割

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With the increase of on-orbit manipulations and space conflictions, missions such as tracking and capturing the target spacecrafts are aroused. Unlike cooperative spacecrafts, fixing beacons or any other marks on the targets is impossible. Due to the unknown shape and geometry features of non-cooperative spacecraft, in order to localize the target and obtain the latitude, we need to segment the target image and recognize the target from the background. The data and errors during the following procedures such as feature extraction and matching can also be reduced. Multi-resolution analysis of wavelet theory reflects human beings' recognition towards images from low resolution to high resolution, hi addition, spacecraft is the only man-made object in the image compared to the natural background and the differences will be certainly observed between the fractal dimensions of target and background. Combined wavelet transform and fractal dimension, in this paper, we proposed a new segmentation algorithm for the images which contains complicated background such as the universe and planet surfaces. At first, Daubechies wavelet basis is applied to decompose the image in both x axis and y axis, thus obtain four sub-images. Then, calculate the fractal dimensions in four sub-images using different methods; after analyzed the results of fractal dimensions in sub-images, we choose Differential Box Counting in low resolution image as the principle to segment the texture which has the greatest divergences between different sub-images. This paper also presents the results of experiments by using the algorithm above. It is demonstrated that an accurate texture segmentation result can be obtained using the proposed technique.
机译:随着在轨操纵和空间冲突的增加,诸如跟踪和捕获目标航天器之类的任务引起了。与合作航天器不同,在目标上固定信标或任何其他标记是不可能的。由于非合作航天器的未知形状和几何特征,为了定位目标并获得纬度,我们需要对目标图像进行分割并从背景中识别目标。还可以减少以下过程中的数据和错误,例如特征提取和匹配。小波理论的多分辨率分析反映了人类对图像的认识,从低分辨率到高分辨率。此外,与自然背景相比,航天器是图像中唯一的人造物体,在分形之间肯定会观察到差异目标和背景的尺寸。结合小波变换和分形维数,提出了一种包含宇宙和行星表面等复杂背景的图像分割算法。首先,应用Daubechies小波基分解x轴和y轴上的图像,从而获得四个子图像。然后,使用不同的方法计算四个子图像中的分形维数;在分析了子图像的分形维数结果之后,我们选择低分辨率图像中的差分盒计数作为原理,对不同子图像之间差异最大的纹理进行分割。本文还介绍了使用上述算法的实验结果。证明了使用所提出的技术可以得到准确的纹理分割结果。

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